ID 原文 译文
23465 该文基于 Laplace 相似度量的构造方法，针对两阶段显著目标检测中显著种子的不同类型(稀疏或稠密)，提出了相应的显著性扩散模型，从而实现了基于扩散的两阶段互补的显著目标检测。 Based on Laplace similarity metrics, corresponding diffusion-based saliency models are proposed according to different clusters (sparse or dense) of salient seeds in the two-stage detection, a diffusion-based two-stage complementary method for salient object detection is therefore investigated.
23466 尤其是第 2 阶段扩散模型中汇点的融入，一方面更好地抑制了显著性图中的背景，同时对于控制因子α的取值更加稳健。 Especially for the introduction of sink points in the second stage, saliency maps obtained by this proposed method can well restrain background parts, as well as become more robust with the change of control factorα.
23467 实验结果表明，当显著种子确定时，不同的扩散模型会导致显著性扩散程度的差异。 Experiments show that different diffusion models will cause diversities of saliency diffusion degree when salient seeds are determined.
23468 基于带汇点 Laplace 的两阶段互补的扩散模型较其他扩散模型更有效、更稳健。 In addition, the two-stage Laplace-based diffusion model with sink points is more effective and robust than other two-stage diffusion models.
23469 同时，从多项评价指标分析，该算法与目前流行的 5 种显著目标检测算法相比，具有较大优势。 Meanwhile, the proposed algorithm is superior over the existing five state-of-the-art methods in terms of different metrics.
23470 这表明此种用于图像检索或分类的 Laplace 相似度量的构造方法在显著目标检测中也是适用的。 This exactly shows that the similarity metrics method applied to image retrieval and classification is also available for salient objects detection.
23471 现实世界中的网络结构呈现出重叠社区的特征。 Overlapping is one of the most important characteristics of real-world networks.
23472 在研究经典的标签算法的基础上，该文提出基于贡献函数的重叠社区发现算法。 Based on the classic labeling algorithm, the overlapping-community orientated label propagation algorithm based on contribution function is proposed.
23473 算法将每个节点用三元组(阈值、标签、从属系数)集合来表示。 In this algorithm, each node is indicated by a set of triples (threshold, label, and coefficient).
23474 节点的阈值是每次迭代过程中标签淘汰的依据，该值由多元线性方程自动计算而来。 The threshold value of every node is used as a metric for labels decision, which is calculated automatically by multiple linear regression equation.